Modeling Discrete Optimization

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About this course: Learn a new way to approach problem solving by stating the problem and letting powerful constraint solving software do the rest. This class teaches you the art of encoding complex discrete optimization problems in the MiniZinc modeling language and then shows you how to effortlessly solve them by leveraging state-of-the-art open-source constraint solving software.

Who is this class for: This course is primarily aimed at third- and fourth-year undergraduates interested in Artificial Intelligence, Computer Science, and Mathematics. Students are expected to have basic programming skills and a general comfort with mathematics. Knowledge of fundamental computer algori…

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Didn't find what you were looking for? See also: Mathematics, Algorithms, Computer Science, Programming, and Hour of Code.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: Learn a new way to approach problem solving by stating the problem and letting powerful constraint solving software do the rest. This class teaches you the art of encoding complex discrete optimization problems in the MiniZinc modeling language and then shows you how to effortlessly solve them by leveraging state-of-the-art open-source constraint solving software.

Who is this class for: This course is primarily aimed at third- and fourth-year undergraduates interested in Artificial Intelligence, Computer Science, and Mathematics. Students are expected to have basic programming skills and a general comfort with mathematics. Knowledge of fundamental computer algorithms is helpful for following some of the optional course content.

Created by:   The University of Melbourne
  • Taught by:    Prof. Peter James Stuckey, Professor

    Computing and Information Systems
  • Taught by:    Dr. Carleton Coffrin, Adjunct Lecturer

    Computing and Information Systems
Level Intermediate Commitment 5 weeks of study, 5-10 hours per week. The last 2 weeks are optional. Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.3 stars Average User Rating 4.3See all 2 reviews Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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About The University of Melbourne The University of Melbourne is an internationally recognised research intensive University with a strong tradition of excellence in teaching, research, and community engagement. Established in 1853, it is Australia's second oldest University.

Syllabus


WEEK 1


Taming the Dragon, First Steps in MiniZinc
In this first module, you will learn the basics of MiniZinc, a high-level modeling language for discrete optimization problems. Combining the simplicity of MiniZinc with the power of open-source industrial solving technologies, you will learn how to solve tric... 


18 videos, 5 readings expand
Graded: Workshop: First Steps Solution

WEEK 2


Core Decisions
In this module you will examine some of the archetypal forms of decisions that need to be made in discrete optimization problems and how to represent them in MiniZinc. After this module Sudoku problems will never bother you again. 


13 videos, 3 readings expand
Graded: Workshop: Temperature Solution

WEEK 3


Multiple Perspectives
In this module you will see how discrete optimization problems can often be seen from multiple viewpoints, and modelled completely differently from each viewpoint. Each viewpoint may have strengths and weaknesses, and indeed the different models can be combine... 


9 videos, 1 reading expand
Graded: Workshop: Composition Solution

WEEK 4


The Power of Predicates
You will learn how to encapsulate a complex constraint definition in a predicate definition to enable its reuse. This will enable the construction of far more complex models. You will learn methods to discover what is going wrong with your model and how to f... 


14 videos, 2 readings expand
Graded: Workshop: Lineup Solution

WEEK 5


Challenging Applications
You will learn how to tackle challenging scheduling and packing problems, and the important combinatorial substructures that underly them. You will see how to model some of the complex constraints that arise in these applications. The assignment will tackle a... 


12 videos, 1 reading expand
Graded: Workshop: Pack Bend Solution

WEEK 6


Other Topics (optional)
In this optional module you will get more insight into the type system of MiniZinc and how MiniZinc models are transformed to a form suitable for solving. Option types are used by MiniZinc to allow flexible specification of loops, and you may have experienced ... 


6 videos, 1 reading expand

WEEK 7


Under the Hood (optional)
This optional module gives insight into how the solvers used by MiniZinc actually work. With greater understanding of the solvers you can create more efficient models. Constraint programming (CP) solvers, like Gecode, also allow search to be programmed in the ... 


11 videos, 2 readings expand
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